PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2512.19350v1
• Date: Mon, 22 Dec 2025 12:49:12 GMT
• Title: PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Models
• Authors: A. B. M. Ashikur Rahman, Saeed Anwar, Muhammad Usman, Irfan Ahmad, Ajmal Mian,
• Abstract summary: Sycophancy is a tendency of AI models to agree with user input at the expense of factual accuracy or in contradiction of visual evidence.<n>We introduce a comprehensive evaluation benchmark, textitPENDULUM, comprising approximately 2,000 human-curated Visual Question Answering pairs.<n>We observe substantial variability in model robustness and a pronounced susceptibility to sycophantic and hallucinatory behavior.
• Score: 43.767942065379366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sycophancy, an excessive tendency of AI models to agree with user input at the expense of factual accuracy or in contradiction of visual evidence, poses a critical and underexplored challenge for multimodal large language models (MLLMs). While prior studies have examined this behavior in text-only settings of large language models, existing research on visual or multimodal counterparts remains limited in scope and depth of analysis. To address this gap, we introduce a comprehensive evaluation benchmark, \textit{PENDULUM}, comprising approximately 2,000 human-curated Visual Question Answering pairs specifically designed to elicit sycophantic responses. The benchmark spans six distinct image domains of varying complexity, enabling a systematic investigation of how image type and inherent challenges influence sycophantic tendencies. Through extensive evaluation of state-of-the-art MLLMs. we observe substantial variability in model robustness and a pronounced susceptibility to sycophantic and hallucinatory behavior. Furthermore, we propose novel metrics to quantify sycophancy in visual reasoning, offering deeper insights into its manifestations across different multimodal contexts. Our findings highlight the urgent need for developing sycophancy-resilient architectures and training strategies to enhance factual consistency and reliability in future MLLMs. Our proposed dataset with MLLMs response are available at https://github.com/ashikiut/pendulum/.

Related papers

• Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing [10.66971486730557]
  Multimodal Large Language Models (MLLMs) have shown substantial capabilities in integrating visual and textual information, yet frequently rely on spurious correlations.<n>This paper addresses the critical challenge of superficial correlation bias in MLLMs through a novel causal mediation-based debiasing framework.
  arXiv  Detail & Related papers  (2025-09-18T19:01:11Z)
• On Evaluating the Adversarial Robustness of Foundation Models for Multimodal Entity Linking [11.268639885321884]
  We conduct the first comprehensive evaluation of the robustness of mainstream MEL models under different adversarial attack scenarios.<n>Experiments on five datasets demonstrate that LLM-RetLink improves the accuracy of MEL by 0.4%-35.7%.<n>This research highlights a previously unexplored facet of MEL robustness, constructs and releases the first MEL adversarial example dataset.
  arXiv  Detail & Related papers  (2025-08-21T11:57:37Z)
• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• KnowDR-REC: A Benchmark for Referring Expression Comprehension with Real-World Knowledge [1.5833270109954136]
  We propose KnowDR-REC, built upon real-world knowledge, requiring fine-grained multimodal reasoning across text and image.<n>We evaluate 16 state-of-the-art multimodal models on KnowDR-REC, with experimental results showing that existing MLLMs still struggle with knowledge-driven visual grounding tasks.
  arXiv  Detail & Related papers  (2025-08-12T19:43:44Z)
• VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning [63.0285363282581]
  Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information.<n>We introduce VOILA, a benchmark designed to evaluate MLLMs' perceptual understanding and abstract relational reasoning.<n>We reveal that current MLLMs struggle to comprehend inter-image relationships and exhibit limited capabilities in high-level relational reasoning.
  arXiv  Detail & Related papers  (2025-02-25T23:36:19Z)
• Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs [65.93003087656754]
  VisFactor is a benchmark that digitizes 20 vision-centric subtests from a well-established cognitive psychology assessment.<n>We evaluate 20 frontier Multimodal Large Language Models (MLLMs) from GPT, Gemini, Claude, LLaMA, Qwen, and SEED families.<n>The best-performing model achieves a score of only 25.19 out of 100, with consistent failures on tasks such as mental rotation, spatial relation inference, and figure-ground discrimination.
  arXiv  Detail & Related papers  (2025-02-23T04:21:32Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research.<n> multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks.<n>This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models [34.647839550142834]
  We introduce GePBench, a novel benchmark designed to assess the geometric perception abilities of MLLMs.<n>Our evaluations reveal that current state-of-the-art MLLMs exhibit significant deficiencies in geometric perception tasks.<n>We show that models trained with GePBench data demonstrate substantial improvements on a wide range of benchmark tasks.
  arXiv  Detail & Related papers  (2024-12-30T16:01:43Z)
• Sycophancy in Large Language Models: Causes and Mitigations [0.0]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Their tendency to exhibit sycophantic behavior poses significant risks to their reliability and ethical deployment. This paper provides a technical survey of sycophancy in LLMs, analyzing its causes, impacts, and potential mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-22T16:56:49Z)
• Cross-Modal Consistency in Multimodal Large Language Models [33.229271701817616]
  We introduce a novel concept termed cross-modal consistency. Our experimental findings reveal a pronounced inconsistency between the vision and language modalities within GPT-4V. Our research yields insights into the appropriate utilization of such models and hints at potential avenues for enhancing their design.
  arXiv  Detail & Related papers  (2024-11-14T08:22:42Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.